MEE 1107
Pattern
Recogni
tion
Module
-
I
Basics of pattern recognition
: Overview of pattern recognition,
Pattern Recognition Systems,
Classification and Description, Pattterns and Feature Extraction, Training and Learning methods,
Pattern
Recognition approaches.
Module
-
II
Bayesian decision theory
: Classifiers, Discriminant functions, Decision surfaces, Normal density
and discriminant functions, Discrete features
,
Parameter estimation methods
,
Maximum
-
Likelihood
estimation, Gaussian mixt
ure models, Expectation
-
maximization method
,
Bayesian estimation
Module
-
I
II
Hidden Markov models for sequential pattern classification
:
Discrete hidden Markov models
,
Contin
u
ous density hidden Markov models
,
Dimension reduction methods
,
Fisher
discriminant
analysis
,
Principal component analysis
Module
-
I
V
Non
-
parametric techniques for density
estimation
:
Parzen
-
window method
,
K
-
Nearest Neighbour
method
Module
-
V
Linear discriminant function based
classifiers
:
Perceptron
,
Support vector mach
ines
,
Multicategory Generalization
Module
-
V
I
Non
-
metric methods for pattern classification
:
Non
-
numeric data or nominal data
,
Decision trees
Module
-
VII
Unsupervised learning and
clustering
:
Criterion functions for clustering
,
Algorithms for
clustering: K
-
means, Hierarchical and other methods
,
Cluster validation
Text Books:
1.
R.O.Duda, P.E.Hart and D.G.Stork, Pattern Classification, John Wiley, 2001
Reference Books:
2. S.Theodoridis and K.Koutroumbas
, Pattern Recognition, 4th Ed., Academic Press, 2009
3. C.M.Bishop, Pattern Recognition and Machine Learning, Springer, 2006
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